Overview

Dataset statistics

Number of variables20
Number of observations1143012
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory215.4 MiB
Average record size in memory197.6 B

Variable types

Text1
Numeric18
Categorical1

Alerts

acousticness is highly overall correlated with energyHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with energyHigh correlation
listenability has 822641 (72.0%) zerosZeros
lag_1 has 828970 (72.5%) zerosZeros
lag_2 has 835246 (73.1%) zerosZeros
lag_3 has 841494 (73.6%) zerosZeros
key has 134181 (11.7%) zerosZeros
instrumentalness has 495414 (43.3%) zerosZeros

Reproduction

Analysis started2023-12-28 20:28:25.009896
Analysis finished2023-12-28 20:32:04.897792
Duration3 minutes and 39.89 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Distinct22412
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:05.127468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters25146264
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0RNxWy0PC3AyH4ThH3aGK6
2nd row0RNxWy0PC3AyH4ThH3aGK6
3rd row0RNxWy0PC3AyH4ThH3aGK6
4th row0RNxWy0PC3AyH4ThH3aGK6
5th row0RNxWy0PC3AyH4ThH3aGK6
ValueCountFrequency (%)
0rnxwy0pc3ayh4thh3agk6 51
 
< 0.1%
3ycbx1qqgrnxmspezlc1ct 51
 
< 0.1%
6kd1sngpkfx9lwagd1fg92 51
 
< 0.1%
5rcvlmvx2xtfcp2ta5pw7x 51
 
< 0.1%
5divwgtej2fpixay9e7zkn 51
 
< 0.1%
0x0ffsap6pkdodghofroof 51
 
< 0.1%
0hsc0siaxoxxbzbt3ms2oj 51
 
< 0.1%
3aejmh1cxkejgh52claxqp 51
 
< 0.1%
1yv6pmpkvldcifbm9kanaz 51
 
< 0.1%
2wayw84ywij5nscpgseu2r 51
 
< 0.1%
Other values (22402) 1142502
> 99.9%
2023-12-28T21:32:05.640723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 540702
 
2.2%
4 540192
 
2.1%
5 538203
 
2.1%
3 528870
 
2.1%
1 528564
 
2.1%
2 528258
 
2.1%
6 527850
 
2.1%
7 502707
 
2.0%
9 398004
 
1.6%
T 395811
 
1.6%
Other values (52) 20117103
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10083873
40.1%
Uppercase Letter 10046235
40.0%
Decimal Number 5016156
19.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 395811
 
3.9%
H 395301
 
3.9%
C 393567
 
3.9%
F 392241
 
3.9%
M 389436
 
3.9%
Z 388365
 
3.9%
J 388314
 
3.9%
W 387906
 
3.9%
V 387651
 
3.9%
L 387549
 
3.9%
Other values (16) 6140094
61.1%
Lowercase Letter
ValueCountFrequency (%)
h 395760
 
3.9%
t 392955
 
3.9%
o 392343
 
3.9%
u 392343
 
3.9%
c 391323
 
3.9%
l 390660
 
3.9%
y 390558
 
3.9%
d 389997
 
3.9%
p 389844
 
3.9%
q 389589
 
3.9%
Other values (16) 6168501
61.2%
Decimal Number
ValueCountFrequency (%)
0 540702
10.8%
4 540192
10.8%
5 538203
10.7%
3 528870
10.5%
1 528564
10.5%
2 528258
10.5%
6 527850
10.5%
7 502707
10.0%
9 398004
7.9%
8 382806
7.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 20130108
80.1%
Common 5016156
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 395811
 
2.0%
h 395760
 
2.0%
H 395301
 
2.0%
C 393567
 
2.0%
t 392955
 
2.0%
o 392343
 
1.9%
u 392343
 
1.9%
F 392241
 
1.9%
c 391323
 
1.9%
l 390660
 
1.9%
Other values (42) 16197804
80.5%
Common
ValueCountFrequency (%)
0 540702
10.8%
4 540192
10.8%
5 538203
10.7%
3 528870
10.5%
1 528564
10.5%
2 528258
10.5%
6 527850
10.5%
7 502707
10.0%
9 398004
7.9%
8 382806
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25146264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 540702
 
2.2%
4 540192
 
2.1%
5 538203
 
2.1%
3 528870
 
2.1%
1 528564
 
2.1%
2 528258
 
2.1%
6 527850
 
2.1%
7 502707
 
2.0%
9 398004
 
1.6%
T 395811
 
1.6%
Other values (52) 20117103
80.0%

week
Real number (ℝ)

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27
Minimum2
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:05.887865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q114
median27
Q340
95-th percentile50
Maximum52
Range50
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.719608
Coefficient of variation (CV)0.54517066
Kurtosis-1.2009231
Mean27
Median Absolute Deviation (MAD)13
Skewness0
Sum30861324
Variance216.66686
MonotonicityNot monotonic
2023-12-28T21:32:06.159749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 22412
 
2.0%
40 22412
 
2.0%
30 22412
 
2.0%
31 22412
 
2.0%
32 22412
 
2.0%
33 22412
 
2.0%
34 22412
 
2.0%
35 22412
 
2.0%
36 22412
 
2.0%
37 22412
 
2.0%
Other values (41) 918892
80.4%
ValueCountFrequency (%)
2 22412
2.0%
3 22412
2.0%
4 22412
2.0%
5 22412
2.0%
6 22412
2.0%
7 22412
2.0%
8 22412
2.0%
9 22412
2.0%
10 22412
2.0%
11 22412
2.0%
ValueCountFrequency (%)
52 22412
2.0%
51 22412
2.0%
50 22412
2.0%
49 22412
2.0%
48 22412
2.0%
47 22412
2.0%
46 22412
2.0%
45 22412
2.0%
44 22412
2.0%
43 22412
2.0%

listenability
Real number (ℝ)

ZEROS 

Distinct87992
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90755443
Minimum0
Maximum101.69028
Zeros822641
Zeros (%)72.0%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:06.392819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum101.69028
Range101.69028
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.7140998
Coefficient of variation (CV)4.0924265
Kurtosis184.26224
Mean0.90755443
Median Absolute Deviation (MAD)0
Skewness12.601666
Sum1037345.6
Variance13.794537
MonotonicityNot monotonic
2023-12-28T21:32:06.629448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 822641
72.0%
2 111446
 
9.8%
1 50526
 
4.4%
4 28334
 
2.5%
3 23867
 
2.1%
5 7484
 
0.7%
6 6266
 
0.5%
7 1993
 
0.2%
8 1394
 
0.1%
9 421
 
< 0.1%
Other values (87982) 88640
 
7.8%
ValueCountFrequency (%)
0 822641
72.0%
0.0003149623857 1
 
< 0.1%
0.0004355772296 1
 
< 0.1%
0.0004426559356 1
 
< 0.1%
0.0005047672462 1
 
< 0.1%
0.0005487694335 1
 
< 0.1%
0.0005511879551 1
 
< 0.1%
0.0005613038759 1
 
< 0.1%
0.0005888490584 1
 
< 0.1%
0.0006001024461 1
 
< 0.1%
ValueCountFrequency (%)
101.6902838 1
< 0.1%
87.49287134 1
< 0.1%
83.03636795 1
< 0.1%
82.63988258 1
< 0.1%
82 1
< 0.1%
81.86528197 1
< 0.1%
81.3475891 1
< 0.1%
81.21249876 1
< 0.1%
81.08006396 1
< 0.1%
80.81747991 1
< 0.1%

lag_1
Real number (ℝ)

ZEROS 

Distinct86226
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88917552
Minimum0
Maximum101.69028
Zeros828970
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:07.494791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum101.69028
Range101.69028
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.6723184
Coefficient of variation (CV)4.1300264
Kurtosis187.33811
Mean0.88917552
Median Absolute Deviation (MAD)0
Skewness12.702651
Sum1016338.3
Variance13.485922
MonotonicityNot monotonic
2023-12-28T21:32:07.777767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 828970
72.5%
2 109259
 
9.6%
1 49548
 
4.3%
4 27792
 
2.4%
3 23365
 
2.0%
5 7354
 
0.6%
6 6139
 
0.5%
7 1947
 
0.2%
8 1362
 
0.1%
9 414
 
< 0.1%
Other values (86216) 86862
 
7.6%
ValueCountFrequency (%)
0 828970
72.5%
0.0003149623857 1
 
< 0.1%
0.0004426559356 1
 
< 0.1%
0.0005047672462 1
 
< 0.1%
0.0005487694335 1
 
< 0.1%
0.0005511879551 1
 
< 0.1%
0.0005613038759 1
 
< 0.1%
0.0005888490584 1
 
< 0.1%
0.0006001024461 1
 
< 0.1%
0.0006613881593 1
 
< 0.1%
ValueCountFrequency (%)
101.6902838 1
< 0.1%
87.49287134 1
< 0.1%
82.63988258 1
< 0.1%
82 1
< 0.1%
81.86528197 1
< 0.1%
81.3475891 1
< 0.1%
81.21249876 1
< 0.1%
79.8393476 1
< 0.1%
79.65039885 1
< 0.1%
79.4304983 1
< 0.1%

lag_2
Real number (ℝ)

ZEROS 

Distinct84472
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87116531
Minimum0
Maximum101.69028
Zeros835246
Zeros (%)73.1%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:08.050281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.97846652
95-th percentile4
Maximum101.69028
Range101.69028
Interquartile range (IQR)0.97846652

Descriptive statistics

Standard deviation3.6375925
Coefficient of variation (CV)4.175548
Kurtosis191.15259
Mean0.87116531
Median Absolute Deviation (MAD)0
Skewness12.828113
Sum995752.4
Variance13.232079
MonotonicityNot monotonic
2023-12-28T21:32:08.316640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 835246
73.1%
2 107110
 
9.4%
1 48581
 
4.3%
4 27220
 
2.4%
3 22908
 
2.0%
5 7198
 
0.6%
6 6006
 
0.5%
7 1913
 
0.2%
8 1334
 
0.1%
9 405
 
< 0.1%
Other values (84462) 85091
 
7.4%
ValueCountFrequency (%)
0 835246
73.1%
0.0003149623857 1
 
< 0.1%
0.0004426559356 1
 
< 0.1%
0.0005047672462 1
 
< 0.1%
0.0005487694335 1
 
< 0.1%
0.0005511879551 1
 
< 0.1%
0.0005613038759 1
 
< 0.1%
0.0005888490584 1
 
< 0.1%
0.0006001024461 1
 
< 0.1%
0.0006613881593 1
 
< 0.1%
ValueCountFrequency (%)
101.6902838 1
< 0.1%
87.49287134 1
< 0.1%
82.63988258 1
< 0.1%
82 1
< 0.1%
81.86528197 1
< 0.1%
81.3475891 1
< 0.1%
81.21249876 1
< 0.1%
79.8393476 1
< 0.1%
79.65039885 1
< 0.1%
79.4304983 1
< 0.1%

lag_3
Real number (ℝ)

ZEROS 

Distinct82821
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85420497
Minimum0
Maximum101.69028
Zeros841494
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:08.573261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.727111
95-th percentile4
Maximum101.69028
Range101.69028
Interquartile range (IQR)0.727111

Descriptive statistics

Standard deviation3.6095841
Coefficient of variation (CV)4.2256651
Kurtosis194.89997
Mean0.85420497
Median Absolute Deviation (MAD)0
Skewness12.954065
Sum976366.53
Variance13.029097
MonotonicityNot monotonic
2023-12-28T21:32:08.833650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 841494
73.6%
2 104923
 
9.2%
1 47552
 
4.2%
4 26679
 
2.3%
3 22412
 
2.0%
5 7056
 
0.6%
6 5881
 
0.5%
7 1869
 
0.2%
8 1319
 
0.1%
9 402
 
< 0.1%
Other values (82811) 83425
 
7.3%
ValueCountFrequency (%)
0 841494
73.6%
0.0003149623857 1
 
< 0.1%
0.0004426559356 1
 
< 0.1%
0.0005047672462 1
 
< 0.1%
0.0005487694335 1
 
< 0.1%
0.0005511879551 1
 
< 0.1%
0.0005613038759 1
 
< 0.1%
0.0005888490584 1
 
< 0.1%
0.0006001024461 1
 
< 0.1%
0.0006613881593 1
 
< 0.1%
ValueCountFrequency (%)
101.6902838 1
< 0.1%
87.49287134 1
< 0.1%
82.63988258 1
< 0.1%
82 1
< 0.1%
81.86528197 1
< 0.1%
81.3475891 1
< 0.1%
81.21249876 1
< 0.1%
79.8393476 1
< 0.1%
79.65039885 1
< 0.1%
79.4304983 1
< 0.1%

popularity
Real number (ℝ)

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.375602
Minimum51
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:09.096414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile51
Q155
median60
Q367
95-th percentile77
Maximum99
Range48
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.0480704
Coefficient of variation (CV)0.13112817
Kurtosis0.02393152
Mean61.375602
Median Absolute Deviation (MAD)6
Skewness0.77702128
Sum70153050
Variance64.771437
MonotonicityNot monotonic
2023-12-28T21:32:09.375403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
51 70227
 
6.1%
52 69003
 
6.0%
53 67932
 
5.9%
54 65586
 
5.7%
55 61404
 
5.4%
56 60894
 
5.3%
57 55794
 
4.9%
58 54468
 
4.8%
59 54315
 
4.8%
60 47328
 
4.1%
Other values (39) 536061
46.9%
ValueCountFrequency (%)
51 70227
6.1%
52 69003
6.0%
53 67932
5.9%
54 65586
5.7%
55 61404
5.4%
56 60894
5.3%
57 55794
4.9%
58 54468
4.8%
59 54315
4.8%
60 47328
4.1%
ValueCountFrequency (%)
99 51
 
< 0.1%
98 51
 
< 0.1%
97 102
 
< 0.1%
96 51
 
< 0.1%
95 51
 
< 0.1%
94 153
 
< 0.1%
93 51
 
< 0.1%
92 204
< 0.1%
91 408
< 0.1%
90 306
< 0.1%

duration_ms
Real number (ℝ)

Distinct14260
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230138.31
Minimum30622
Maximum4120258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:09.632413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum30622
5-th percentile147439
Q1191493
median220667
Q3256240
95-th percentile338493
Maximum4120258
Range4089636
Interquartile range (IQR)64747

Descriptive statistics

Standard deviation72094.041
Coefficient of variation (CV)0.31326397
Kurtosis397.62795
Mean230138.31
Median Absolute Deviation (MAD)31893.5
Skewness9.3389523
Sum2.6305085 × 1011
Variance5.1975508 × 109
MonotonicityNot monotonic
2023-12-28T21:32:09.895393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235800 561
 
< 0.1%
235493 561
 
< 0.1%
212000 561
 
< 0.1%
203507 510
 
< 0.1%
160000 459
 
< 0.1%
240000 459
 
< 0.1%
235000 459
 
< 0.1%
214400 459
 
< 0.1%
209440 459
 
< 0.1%
228293 459
 
< 0.1%
Other values (14250) 1138065
99.6%
ValueCountFrequency (%)
30622 51
< 0.1%
32000 51
< 0.1%
33107 51
< 0.1%
37640 51
< 0.1%
38333 51
< 0.1%
39640 51
< 0.1%
40594 51
< 0.1%
41500 51
< 0.1%
44064 51
< 0.1%
44827 51
< 0.1%
ValueCountFrequency (%)
4120258 51
< 0.1%
1421455 51
< 0.1%
1412451 102
< 0.1%
1233667 51
< 0.1%
1093486 51
< 0.1%
1025280 102
< 0.1%
992440 51
< 0.1%
899933 51
< 0.1%
875307 51
< 0.1%
858133 51
< 0.1%

explicit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.7 MiB
0
952578 
1
190434 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1143012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 952578
83.3%
1 190434
 
16.7%

Length

2023-12-28T21:32:10.132880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-28T21:32:10.293566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 952578
83.3%
1 190434
 
16.7%

Most occurring characters

ValueCountFrequency (%)
0 952578
83.3%
1 190434
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1143012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 952578
83.3%
1 190434
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1143012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 952578
83.3%
1 190434
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1143012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 952578
83.3%
1 190434
 
16.7%

danceability
Real number (ℝ)

Distinct860
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59909614
Minimum0
Maximum0.98
Zeros204
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:10.493789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.317
Q10.495
median0.608
Q30.715
95-th percentile0.843
Maximum0.98
Range0.98
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.15825516
Coefficient of variation (CV)0.26415653
Kurtosis-0.22423371
Mean0.59909614
Median Absolute Deviation (MAD)0.109
Skewness-0.30811752
Sum684774.07
Variance0.025044694
MonotonicityNot monotonic
2023-12-28T21:32:10.738640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.664 3774
 
0.3%
0.602 3723
 
0.3%
0.608 3621
 
0.3%
0.689 3570
 
0.3%
0.675 3519
 
0.3%
0.589 3519
 
0.3%
0.554 3519
 
0.3%
0.654 3468
 
0.3%
0.583 3468
 
0.3%
0.634 3366
 
0.3%
Other values (850) 1107465
96.9%
ValueCountFrequency (%)
0 204
< 0.1%
0.0594 51
 
< 0.1%
0.0624 51
 
< 0.1%
0.0657 102
< 0.1%
0.067 51
 
< 0.1%
0.0715 51
 
< 0.1%
0.0753 51
 
< 0.1%
0.0758 51
 
< 0.1%
0.0763 51
 
< 0.1%
0.0768 51
 
< 0.1%
ValueCountFrequency (%)
0.98 102
< 0.1%
0.97 102
< 0.1%
0.969 102
< 0.1%
0.968 51
 
< 0.1%
0.967 51
 
< 0.1%
0.966 102
< 0.1%
0.965 204
< 0.1%
0.964 153
< 0.1%
0.963 204
< 0.1%
0.961 51
 
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct1107
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64801748
Minimum0.000103
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:10.989172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.000103
5-th percentile0.266
Q10.508
median0.672
Q30.816
95-th percentile0.942
Maximum0.999
Range0.998897
Interquartile range (IQR)0.308

Descriptive statistics

Standard deviation0.20957275
Coefficient of variation (CV)0.32340601
Kurtosis-0.26013202
Mean0.64801748
Median Absolute Deviation (MAD)0.152
Skewness-0.53951076
Sum740691.76
Variance0.043920738
MonotonicityNot monotonic
2023-12-28T21:32:11.235177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.716 3060
 
0.3%
0.67 2958
 
0.3%
0.758 2805
 
0.2%
0.72 2754
 
0.2%
0.8 2652
 
0.2%
0.729 2652
 
0.2%
0.748 2601
 
0.2%
0.656 2601
 
0.2%
0.824 2550
 
0.2%
0.873 2499
 
0.2%
Other values (1097) 1115880
97.6%
ValueCountFrequency (%)
0.000103 51
< 0.1%
0.000413 51
< 0.1%
0.000553 102
< 0.1%
0.000711 51
< 0.1%
0.000793 51
< 0.1%
0.000868 51
< 0.1%
0.000891 51
< 0.1%
0.0009 51
< 0.1%
0.000989 51
< 0.1%
0.000996 51
< 0.1%
ValueCountFrequency (%)
0.999 51
 
< 0.1%
0.998 51
 
< 0.1%
0.997 102
 
< 0.1%
0.996 663
0.1%
0.995 816
0.1%
0.994 561
< 0.1%
0.993 612
0.1%
0.992 357
< 0.1%
0.991 561
< 0.1%
0.99 561
< 0.1%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2788238
Minimum0
Maximum11
Zeros134181
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:11.432018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5587349
Coefficient of variation (CV)0.67415299
Kurtosis-1.2807944
Mean5.2788238
Median Absolute Deviation (MAD)3
Skewness0.0030652782
Sum6033759
Variance12.664594
MonotonicityNot monotonic
2023-12-28T21:32:11.618918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 134181
11.7%
7 124083
10.9%
9 120309
10.5%
2 117708
10.3%
1 104652
9.2%
4 95166
8.3%
5 94554
8.3%
11 90168
7.9%
6 77622
6.8%
8 73491
6.4%
Other values (2) 111078
9.7%
ValueCountFrequency (%)
0 134181
11.7%
1 104652
9.2%
2 117708
10.3%
3 38199
 
3.3%
4 95166
8.3%
5 94554
8.3%
6 77622
6.8%
7 124083
10.9%
8 73491
6.4%
9 120309
10.5%
ValueCountFrequency (%)
11 90168
7.9%
10 72879
6.4%
9 120309
10.5%
8 73491
6.4%
7 124083
10.9%
6 77622
6.8%
5 94554
8.3%
4 95166
8.3%
3 38199
 
3.3%
2 117708
10.3%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct9554
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.1962269
Minimum-44.41
Maximum0.642
Zeros0
Zeros (%)0.0%
Negative1142808
Negative (%)> 99.9%
Memory size49.7 MiB
2023-12-28T21:32:11.843391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-44.41
5-th percentile-13.813
Q1-8.6145
median-6.393
Q3-4.85
95-th percentile-3.104
Maximum0.642
Range45.052
Interquartile range (IQR)3.7645

Descriptive statistics

Standard deviation3.7380161
Coefficient of variation (CV)-0.51944112
Kurtosis13.999159
Mean-7.1962269
Median Absolute Deviation (MAD)1.799
Skewness-2.5954729
Sum-8225373.7
Variance13.972765
MonotonicityNot monotonic
2023-12-28T21:32:12.087529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.819 663
 
0.1%
-5.305 561
 
< 0.1%
-7.045 561
 
< 0.1%
-5.507 561
 
< 0.1%
-4.139 510
 
< 0.1%
-4.41 510
 
< 0.1%
-4.998 510
 
< 0.1%
-4.88 510
 
< 0.1%
-4.475 510
 
< 0.1%
-7.063 510
 
< 0.1%
Other values (9544) 1137606
99.5%
ValueCountFrequency (%)
-44.41 51
< 0.1%
-43.82 51
< 0.1%
-43.19 51
< 0.1%
-42.489 51
< 0.1%
-42.363 51
< 0.1%
-42.23 51
< 0.1%
-41.766 51
< 0.1%
-41.378 51
< 0.1%
-41.288 51
< 0.1%
-40.533 51
< 0.1%
ValueCountFrequency (%)
0.642 51
< 0.1%
0.496 51
< 0.1%
0.457 51
< 0.1%
0.161 51
< 0.1%
-0.025 51
< 0.1%
-0.32 102
< 0.1%
-0.387 51
< 0.1%
-0.407 102
< 0.1%
-0.517 51
< 0.1%
-0.532 51
< 0.1%

speechiness
Real number (ℝ)

Distinct1235
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.082913966
Minimum0
Maximum0.944
Zeros204
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:12.316077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0274
Q10.0339
median0.0463
Q30.086
95-th percentile0.292
Maximum0.944
Range0.944
Interquartile range (IQR)0.0521

Descriptive statistics

Standard deviation0.089315201
Coefficient of variation (CV)1.0772033
Kurtosis8.9022721
Mean0.082913966
Median Absolute Deviation (MAD)0.0158
Skewness2.7252623
Sum94771.658
Variance0.0079772052
MonotonicityNot monotonic
2023-12-28T21:32:12.549178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0305 4743
 
0.4%
0.0302 4437
 
0.4%
0.0303 4233
 
0.4%
0.0311 4233
 
0.4%
0.0335 4233
 
0.4%
0.0332 4182
 
0.4%
0.0324 4131
 
0.4%
0.0334 4080
 
0.4%
0.0346 4029
 
0.4%
0.0339 3978
 
0.3%
Other values (1225) 1100733
96.3%
ValueCountFrequency (%)
0 204
< 0.1%
0.0224 102
< 0.1%
0.0225 102
< 0.1%
0.0226 51
 
< 0.1%
0.0227 102
< 0.1%
0.0228 153
< 0.1%
0.0229 204
< 0.1%
0.023 51
 
< 0.1%
0.0231 102
< 0.1%
0.0232 153
< 0.1%
ValueCountFrequency (%)
0.944 51
< 0.1%
0.939 51
< 0.1%
0.935 51
< 0.1%
0.884 51
< 0.1%
0.868 51
< 0.1%
0.835 51
< 0.1%
0.772 51
< 0.1%
0.762 51
< 0.1%
0.758 51
< 0.1%
0.741 51
< 0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct3408
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26775771
Minimum1.39 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:12.791002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.39 × 10-6
5-th percentile0.000619
Q10.0298
median0.165
Q30.455
95-th percentile0.837
Maximum0.996
Range0.99599861
Interquartile range (IQR)0.4252

Descriptive statistics

Standard deviation0.276105
Coefficient of variation (CV)1.0311748
Kurtosis-0.31986198
Mean0.26775771
Median Absolute Deviation (MAD)0.15606
Skewness0.92222325
Sum306050.27
Variance0.07623397
MonotonicityNot monotonic
2023-12-28T21:32:13.042370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.117 2703
 
0.2%
0.105 2550
 
0.2%
0.111 2346
 
0.2%
0.159 2244
 
0.2%
0.139 2193
 
0.2%
0.114 2091
 
0.2%
0.112 2040
 
0.2%
0.192 2040
 
0.2%
0.103 1989
 
0.2%
0.136 1989
 
0.2%
Other values (3398) 1120827
98.1%
ValueCountFrequency (%)
1.39 × 10-651
< 0.1%
1.52 × 10-651
< 0.1%
1.53 × 10-6102
< 0.1%
2.04 × 10-651
< 0.1%
2.27 × 10-651
< 0.1%
2.38 × 10-651
< 0.1%
2.65 × 10-6102
< 0.1%
2.7 × 10-651
< 0.1%
2.73 × 10-651
< 0.1%
3.11 × 10-651
< 0.1%
ValueCountFrequency (%)
0.996 306
< 0.1%
0.995 153
 
< 0.1%
0.994 459
< 0.1%
0.993 357
< 0.1%
0.992 459
< 0.1%
0.991 204
< 0.1%
0.99 408
< 0.1%
0.989 51
 
< 0.1%
0.988 153
 
< 0.1%
0.987 408
< 0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct4232
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0402202
Minimum0
Maximum0.998
Zeros495414
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:13.288852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.435 × 10-6
Q30.000523
95-th percentile0.297
Maximum0.998
Range0.998
Interquartile range (IQR)0.000523

Descriptive statistics

Standard deviation0.15282449
Coefficient of variation (CV)3.7996948
Kurtosis19.939868
Mean0.0402202
Median Absolute Deviation (MAD)3.435 × 10-6
Skewness4.48272
Sum45972.172
Variance0.023355323
MonotonicityNot monotonic
2023-12-28T21:32:13.533700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 495414
43.3%
1.68 × 10-6969
 
0.1%
0.000103 969
 
0.1%
1.33 × 10-5867
 
0.1%
0.00106 867
 
0.1%
1.65 × 10-6816
 
0.1%
1.3 × 10-6816
 
0.1%
1.03 × 10-5816
 
0.1%
1.02 × 10-6765
 
0.1%
1.29 × 10-6765
 
0.1%
Other values (4222) 639948
56.0%
ValueCountFrequency (%)
0 495414
43.3%
1 × 10-6357
 
< 0.1%
1.01 × 10-6765
 
0.1%
1.02 × 10-6765
 
0.1%
1.03 × 10-6714
 
0.1%
1.04 × 10-6459
 
< 0.1%
1.05 × 10-6612
 
0.1%
1.06 × 10-6510
 
< 0.1%
1.07 × 10-6510
 
< 0.1%
1.08 × 10-6510
 
< 0.1%
ValueCountFrequency (%)
0.998 51
< 0.1%
0.995 51
< 0.1%
0.993 51
< 0.1%
0.988 51
< 0.1%
0.986 51
< 0.1%
0.984 102
< 0.1%
0.983 51
< 0.1%
0.982 51
< 0.1%
0.981 51
< 0.1%
0.98 102
< 0.1%

liveness
Real number (ℝ)

Distinct1573
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19314138
Minimum0.012
Maximum0.997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:13.777015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.0589
Q10.0944
median0.125
Q30.24
95-th percentile0.561
Maximum0.997
Range0.985
Interquartile range (IQR)0.1456

Descriptive statistics

Standard deviation0.16793975
Coefficient of variation (CV)0.86951719
Kurtosis6.3052842
Mean0.19314138
Median Absolute Deviation (MAD)0.045
Skewness2.3739434
Sum220762.92
Variance0.028203761
MonotonicityNot monotonic
2023-12-28T21:32:14.027994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.109 13719
 
1.2%
0.11 12750
 
1.1%
0.111 11985
 
1.0%
0.107 11985
 
1.0%
0.105 11577
 
1.0%
0.104 11220
 
1.0%
0.108 11067
 
1.0%
0.106 10608
 
0.9%
0.102 10455
 
0.9%
0.114 10149
 
0.9%
Other values (1563) 1027497
89.9%
ValueCountFrequency (%)
0.012 51
< 0.1%
0.0137 51
< 0.1%
0.015 51
< 0.1%
0.0165 51
< 0.1%
0.0167 51
< 0.1%
0.0181 51
< 0.1%
0.019 102
< 0.1%
0.0193 51
< 0.1%
0.0196 51
< 0.1%
0.0197 51
< 0.1%
ValueCountFrequency (%)
0.997 51
 
< 0.1%
0.996 51
 
< 0.1%
0.994 51
 
< 0.1%
0.993 51
 
< 0.1%
0.992 51
 
< 0.1%
0.991 51
 
< 0.1%
0.99 51
 
< 0.1%
0.989 51
 
< 0.1%
0.988 153
< 0.1%
0.986 51
 
< 0.1%

valence
Real number (ℝ)

Distinct1291
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51800029
Minimum0
Maximum0.991
Zeros255
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:14.268421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.131
Q10.325
median0.512
Q30.713
95-th percentile0.927
Maximum0.991
Range0.991
Interquartile range (IQR)0.388

Descriptive statistics

Standard deviation0.24447154
Coefficient of variation (CV)0.47195253
Kurtosis-0.96703816
Mean0.51800029
Median Absolute Deviation (MAD)0.193
Skewness0.044741927
Sum592080.54
Variance0.059766335
MonotonicityNot monotonic
2023-12-28T21:32:14.520801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.962 4335
 
0.4%
0.961 3213
 
0.3%
0.964 3162
 
0.3%
0.963 2754
 
0.2%
0.506 2499
 
0.2%
0.965 2448
 
0.2%
0.393 2346
 
0.2%
0.565 2346
 
0.2%
0.527 2193
 
0.2%
0.354 2193
 
0.2%
Other values (1281) 1115523
97.6%
ValueCountFrequency (%)
0 255
< 0.1%
1 × 10-5255
< 0.1%
0.00237 51
 
< 0.1%
0.00898 51
 
< 0.1%
0.022 51
 
< 0.1%
0.0222 51
 
< 0.1%
0.0242 51
 
< 0.1%
0.0254 51
 
< 0.1%
0.0256 51
 
< 0.1%
0.0259 51
 
< 0.1%
ValueCountFrequency (%)
0.991 51
 
< 0.1%
0.987 51
 
< 0.1%
0.985 51
 
< 0.1%
0.984 51
 
< 0.1%
0.983 51
 
< 0.1%
0.982 51
 
< 0.1%
0.981 51
 
< 0.1%
0.98 255
< 0.1%
0.979 153
 
< 0.1%
0.978 408
< 0.1%

tempo
Real number (ℝ)

Distinct16956
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.91194
Minimum0
Maximum220.099
Zeros204
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:14.764621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78.842
Q197.988
median120.041
Q3140.078
95-th percentile175.077
Maximum220.099
Range220.099
Interquartile range (IQR)42.09

Descriptive statistics

Standard deviation29.614607
Coefficient of variation (CV)0.24291801
Kurtosis-0.36755572
Mean121.91194
Median Absolute Deviation (MAD)21.19
Skewness0.38788642
Sum1.3934682 × 108
Variance877.02493
MonotonicityNot monotonic
2023-12-28T21:32:14.999888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.972 561
 
< 0.1%
120.012 510
 
< 0.1%
130 510
 
< 0.1%
100.005 510
 
< 0.1%
139.997 459
 
< 0.1%
127.96 459
 
< 0.1%
119.975 459
 
< 0.1%
120.04 459
 
< 0.1%
119.994 408
 
< 0.1%
130.021 408
 
< 0.1%
Other values (16946) 1138269
99.6%
ValueCountFrequency (%)
0 204
< 0.1%
34.717 51
 
< 0.1%
37.114 51
 
< 0.1%
43.26 51
 
< 0.1%
43.509 51
 
< 0.1%
48.637 51
 
< 0.1%
48.718 51
 
< 0.1%
49.034 51
 
< 0.1%
49.731 51
 
< 0.1%
49.995 51
 
< 0.1%
ValueCountFrequency (%)
220.099 51
< 0.1%
220.065 102
< 0.1%
219.827 51
< 0.1%
215.918 51
< 0.1%
214.67 51
< 0.1%
214.016 51
< 0.1%
211.842 51
< 0.1%
211.27 51
< 0.1%
210.857 102
< 0.1%
210.851 51
< 0.1%

release_date
Real number (ℝ)

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.1505
Minimum1929
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.7 MiB
2023-12-28T21:32:15.228246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1929
5-th percentile1975
Q12000
median2011
Q32017
95-th percentile2020
Maximum2021
Range92
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.920591
Coefficient of variation (CV)0.0069389564
Kurtosis0.88531386
Mean2006.1505
Median Absolute Deviation (MAD)7
Skewness-1.256732
Sum2.2930541 × 109
Variance193.78285
MonotonicityNot monotonic
2023-12-28T21:32:15.466273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019 72369
 
6.3%
2020 72216
 
6.3%
2018 70023
 
6.1%
2017 60843
 
5.3%
2016 60537
 
5.3%
2015 52989
 
4.6%
2012 44370
 
3.9%
2014 44166
 
3.9%
2013 43401
 
3.8%
2010 35037
 
3.1%
Other values (62) 587061
51.4%
ValueCountFrequency (%)
1929 51
 
< 0.1%
1943 51
 
< 0.1%
1944 153
 
< 0.1%
1950 102
 
< 0.1%
1954 51
 
< 0.1%
1955 51
 
< 0.1%
1956 510
< 0.1%
1957 306
< 0.1%
1958 663
0.1%
1959 255
 
< 0.1%
ValueCountFrequency (%)
2021 29376
2.6%
2020 72216
6.3%
2019 72369
6.3%
2018 70023
6.1%
2017 60843
5.3%
2016 60537
5.3%
2015 52989
4.6%
2014 44166
3.9%
2013 43401
3.8%
2012 44370
3.9%

Interactions

2023-12-28T21:31:41.789934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:29:58.425579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:10.045408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:18.195688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:23.450445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:28.671577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:33.714801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:39.347292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:45.501479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:50.462335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:55.366400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:01.174456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:06.029971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:11.050689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:16.008460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:21.394572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:26.402481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:32.616863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:42.409926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:29:59.022900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:10.596122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:18.473967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:23.733575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:28.942712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:34.037500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:39.662811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:45.790834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:50.749925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:55.678237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:01.449435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:06.354172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:11.345255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:16.277348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:21.687920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:26.685840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:33.090029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:42.932118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:29:59.760292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:11.176147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:18.758833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:24.011994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:29.216354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:34.334452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:39.942550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:46.077532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:51.032997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:55.948075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:01.743046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:06.629878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:11.616110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:16.549370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:21.983649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:26.977799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:33.574484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:43.635979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:00.308551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:11.808877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:19.061403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:24.280726image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:29.496424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:34.625479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:40.531696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:46.347758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:51.307553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:56.213660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:02.014984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:06.896242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:11.890340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:16.822651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:22.276440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:27.264704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:34.065378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:44.169720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:01.035708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:12.391682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:19.348870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:24.554051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:29.766708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:34.893830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:40.810750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:46.621485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:51.582516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:56.483542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:02.280705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:07.165723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:12.162945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:17.095325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:22.548504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:27.540237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:34.540087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:44.725987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:01.796288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:13.109382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:19.637573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:24.828852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:30.034241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:35.155992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:41.254281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:46.889505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:51.848619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:56.894931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:02.546472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:07.426553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:12.434076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:17.366168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:22.822245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:27.820690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:35.016438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:45.390466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:02.466508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:14.764028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:19.916150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:25.110774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:30.306955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:35.438252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:41.578128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:47.159432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:52.129540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:57.202595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:02.827495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:07.707109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:12.710040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:17.646130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:23.100374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:28.110080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:35.494303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:45.948054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:03.093139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:15.078648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:20.222488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:25.405062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:30.601057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:35.931572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:41.874105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:47.445303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:52.416832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:57.677870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:03.131477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:08.014403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:13.010647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:17.940288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:23.400085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:28.587796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:35.997418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:46.664683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:03.743250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:15.358955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:20.516728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:25.683851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:30.878717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:36.204939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:42.171005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:47.711902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:52.679400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:58.329957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:03.395062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:08.298654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:13.288181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:18.223776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:23.686581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:28.861438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:36.488381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:47.187281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:04.506951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:15.646498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:20.840631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:26.213574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:31.157301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:36.503497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:42.480358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:47.981828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:52.945303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:58.602993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:03.662378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:08.574409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:13.558247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:18.494879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:23.960374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:29.151580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:36.990778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:47.867860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:05.291269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:15.933326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:21.216922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:26.493853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:31.442288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:36.946621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:42.961337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:48.246559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:53.210843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:58.865526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:03.921981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:08.855513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:13.833189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:18.766672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:24.226673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:29.463966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:37.481577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:48.380865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:05.954235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:16.215376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:21.496630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:26.770867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:31.729687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:37.206228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:43.246210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:48.517448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:53.478966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:59.127378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:04.177303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:09.127276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:14.107737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:19.040643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:24.495373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:29.750989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:37.981313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:49.061922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:06.544384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:16.494217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:21.776420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:27.042869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:32.009924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:37.497711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:43.533857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:48.780240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:53.751447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:59.387019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:04.434973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:09.392589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:14.383803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:19.768637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:24.766978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:30.179222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:38.488988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:49.572822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:07.129313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:16.781593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:22.057760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:27.316233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:32.309686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:37.777287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:43.827041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:49.058490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:54.029031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:59.655755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:04.703240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:09.662285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:14.657206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:20.043245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:25.044111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:30.495193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:38.985376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:50.221045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:07.628283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:17.064222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:22.345947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:27.596124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:32.599174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:38.047908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:44.145167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:49.342194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:54.297043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:59.929103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:04.974012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:09.930508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:14.928818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:20.312579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:25.313687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:30.950276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:39.487781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:50.783714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:08.250367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:17.349587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:22.627700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:27.872436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:32.887766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:38.325829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:44.441301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:49.634169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:54.563353image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:00.198037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:05.242471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:10.211520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:15.205996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:20.583683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:25.585165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:31.391863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:40.021951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:51.320578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:08.732192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:17.638832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:22.899371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:28.144812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:33.164678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:38.604999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:44.747063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:49.905309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:54.832639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:00.459538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:05.506143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:10.495358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:15.472918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:20.856534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:25.859255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:31.673607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:40.522933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:51.985936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:09.308985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:17.916466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:23.181423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:28.409693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:33.433758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:38.891336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:45.038869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:50.181637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:30:55.094325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:00.732056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:05.760858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:10.772627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:15.741705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:21.124634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:26.128030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:32.145246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-28T21:31:41.280898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2023-12-28T21:32:15.653205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyexplicitinstrumentalnesskeylag_1lag_2lag_3listenabilitylivenessloudnesspopularityrelease_datespeechinesstempovalenceweek
acousticness1.0000.044-0.107-0.6080.094-0.110-0.017-0.115-0.114-0.112-0.117-0.048-0.424-0.0190.092-0.141-0.119-0.0850.000
danceability0.0441.000-0.2120.0040.243-0.1330.023-0.097-0.095-0.094-0.098-0.1050.0750.0910.2430.226-0.1210.4490.000
duration_ms-0.107-0.2121.0000.0160.0130.1820.0040.1130.1120.1100.114-0.009-0.082-0.107-0.285-0.185-0.023-0.2100.000
energy-0.6080.0040.0161.0000.129-0.0180.0190.0290.0290.0290.0290.1410.704-0.039-0.0210.2870.1660.3160.000
explicit0.0940.2430.0130.1291.000-0.0880.010-0.061-0.060-0.059-0.0620.0130.0380.1200.2500.397-0.017-0.0710.000
instrumentalness-0.110-0.1330.182-0.018-0.0881.000-0.0020.0750.0740.0730.076-0.064-0.266-0.090-0.290-0.130-0.015-0.1120.000
key-0.0170.0230.0040.0190.010-0.0021.000-0.005-0.004-0.004-0.005-0.0120.007-0.014-0.0050.0460.0060.0320.000
lag_1-0.115-0.0970.1130.029-0.0610.075-0.0051.0000.4420.4360.438-0.019-0.0310.061-0.193-0.081-0.008-0.0390.018
lag_2-0.114-0.0950.1120.029-0.0600.074-0.0040.4421.0000.4460.433-0.019-0.0310.060-0.191-0.080-0.008-0.0380.038
lag_3-0.112-0.0940.1100.029-0.0590.073-0.0040.4360.4461.0000.424-0.019-0.0300.059-0.188-0.079-0.008-0.0380.058
listenability-0.117-0.0980.1140.029-0.0620.076-0.0050.4380.4330.4241.000-0.020-0.0320.062-0.196-0.082-0.008-0.039-0.002
liveness-0.048-0.105-0.0090.1410.013-0.064-0.012-0.019-0.019-0.019-0.0201.0000.098-0.036-0.0050.0460.019-0.0080.000
loudness-0.4240.075-0.0820.7040.038-0.2660.007-0.031-0.031-0.030-0.0320.0981.0000.0680.2560.1890.1310.2110.000
popularity-0.0190.091-0.107-0.0390.120-0.090-0.0140.0610.0600.0590.062-0.0360.0681.0000.2860.0590.008-0.0250.000
release_date0.0920.243-0.285-0.0210.250-0.290-0.005-0.193-0.191-0.188-0.196-0.0050.2560.2861.0000.2530.027-0.1000.000
speechiness-0.1410.226-0.1850.2870.397-0.1300.046-0.081-0.080-0.079-0.0820.0460.1890.0590.2531.0000.0900.1200.000
tempo-0.119-0.121-0.0230.166-0.017-0.0150.006-0.008-0.008-0.008-0.0080.0190.1310.0080.0270.0901.0000.0650.000
valence-0.0850.449-0.2100.316-0.071-0.1120.032-0.039-0.038-0.038-0.039-0.0080.211-0.025-0.1000.1200.0651.0000.000
week0.0000.0000.0000.0000.0000.0000.0000.0180.0380.058-0.0020.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-28T21:31:52.398214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-28T21:31:57.548436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

track_idweeklistenabilitylag_1lag_2lag_3popularityduration_msexplicitdanceabilityenergykeyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetemporelease_date
00RNxWy0PC3AyH4ThH3aGK620.00.00.00.05520146700.6730.3770-14.1410.06970.5860.00.3320.71388.9731929
10RNxWy0PC3AyH4ThH3aGK630.00.00.00.05520146700.6730.3770-14.1410.06970.5860.00.3320.71388.9731929
20RNxWy0PC3AyH4ThH3aGK640.00.00.00.05520146700.6730.3770-14.1410.06970.5860.00.3320.71388.9731929
30RNxWy0PC3AyH4ThH3aGK650.00.00.00.05520146700.6730.3770-14.1410.06970.5860.00.3320.71388.9731929
40RNxWy0PC3AyH4ThH3aGK660.00.00.00.05520146700.6730.3770-14.1410.06970.5860.00.3320.71388.9731929
50RNxWy0PC3AyH4ThH3aGK670.00.00.00.05520146700.6730.3770-14.1410.06970.5860.00.3320.71388.9731929
60RNxWy0PC3AyH4ThH3aGK680.00.00.00.05520146700.6730.3770-14.1410.06970.5860.00.3320.71388.9731929
70RNxWy0PC3AyH4ThH3aGK690.00.00.00.05520146700.6730.3770-14.1410.06970.5860.00.3320.71388.9731929
80RNxWy0PC3AyH4ThH3aGK6100.00.00.00.05520146700.6730.3770-14.1410.06970.5860.00.3320.71388.9731929
90RNxWy0PC3AyH4ThH3aGK6110.00.00.00.05520146700.6730.3770-14.1410.06970.5860.00.3320.71388.9731929
track_idweeklistenabilitylag_1lag_2lag_3popularityduration_msexplicitdanceabilityenergykeyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetemporelease_date
114300227Y1N4Q4U3EfDU5Ubw8ws2430.01.02.00.07018760100.5350.3147-12.8230.04080.8950.000150.08740.0663145.0952020
114300327Y1N4Q4U3EfDU5Ubw8ws2440.00.01.02.07018760100.5350.3147-12.8230.04080.8950.000150.08740.0663145.0952020
114300427Y1N4Q4U3EfDU5Ubw8ws2455.00.00.01.07018760100.5350.3147-12.8230.04080.8950.000150.08740.0663145.0952020
114300527Y1N4Q4U3EfDU5Ubw8ws2460.05.00.00.07018760100.5350.3147-12.8230.04080.8950.000150.08740.0663145.0952020
114300627Y1N4Q4U3EfDU5Ubw8ws2472.00.05.00.07018760100.5350.3147-12.8230.04080.8950.000150.08740.0663145.0952020
114300727Y1N4Q4U3EfDU5Ubw8ws2480.02.00.05.07018760100.5350.3147-12.8230.04080.8950.000150.08740.0663145.0952020
114300827Y1N4Q4U3EfDU5Ubw8ws2492.00.02.00.07018760100.5350.3147-12.8230.04080.8950.000150.08740.0663145.0952020
114300927Y1N4Q4U3EfDU5Ubw8ws2500.02.00.02.07018760100.5350.3147-12.8230.04080.8950.000150.08740.0663145.0952020
114301027Y1N4Q4U3EfDU5Ubw8ws2510.00.02.00.07018760100.5350.3147-12.8230.04080.8950.000150.08740.0663145.0952020
114301127Y1N4Q4U3EfDU5Ubw8ws2520.00.00.02.07018760100.5350.3147-12.8230.04080.8950.000150.08740.0663145.0952020